Writing Style Analysis

Please analyze the writing style and personality of the given writing sample. Provide a detailed assessment of their characteristics using the following template. Rate each applicable characteristic on a scale of 1-10 where relevant, or provide a descriptive value. Store the results in a JSON format. {{ "name": "[Author/Character Name]", "vocabulary_complexity": [1-10], "sentence_structure": "[simple/complex/varied]", "paragraph_organization": "[structured/loose/stream-of-consciousness]", "idiom_usage": [1-10], "metaphor_frequency": [1-10], "simile_frequency": [1-10], "tone": "[formal/informal/academic/conversational/etc.]", "punctuation_style": "[minimal/heavy/unconventional]", "contraction_usage": [1-10], "pronoun_preference": "[first-person/third-person/etc.]", "passive_voice_frequency": [1-10], "rhetorical_question_usage": [1-10], "list_usage_tendency": [1-10], "personal_anecdote_inclusion": [1-10], "pop_culture_reference_frequency": [1-10], "technical_jargon_usage": [1-10], "parenthetical_aside_frequency": [1-10], "humor_sarcasm_usage": [1-10], "emotional_expressiveness": [1-10], "emphatic_device_usage": [1-10], "quotation_frequency": [1-10], "analogy_usage": [1-10], "sensory_detail_inclusion": [1-10], "onomatopoeia_usage": [1-10], "alliteration_frequency": [1-10], "word_length_preference": "[short/long/varied]", "foreign_phrase_usage": [1-10], "rhetorical_device_usage": [1-10], "statistical_data_usage": [1-10], "personal_opinion_inclusion": [1-10], "transition_usage": [1-10], "reader_question_frequency": [1-10], "imperative_sentence_usage": [1-10], "dialogue_inclusion": [1-10], "regional_dialect_usage": [1-10], "hedging_language_frequency": [1-10], "language_abstraction": "[concrete/abstract/mixed]", "personal_belief_inclusion": [1-10], "repetition_usage": [1-10], "subordinate_clause_frequency": [1-10], "verb_type_preference": "[active/stative/mixed]", "sensory_imagery_usage": [1-10], "symbolism_usage": [1-10], "digression_frequency": [1-10], "formality_level": [1-10], "reflection_inclusion": [1-10], "irony_usage": [1-10], "neologism_frequency": [1-10], "ellipsis_usage": [1-10], "cultural_reference_inclusion": [1-10], "stream_of_consciousness_usage": [1-10], "psychological_traits": {{ "openness_to_experience": [1-10], "conscientiousness": [1-10], "extraversion": [1-10], "agreeableness": [1-10], "emotional_stability": [1-10], "dominant_motivations": "[achievement/affiliation/power/etc.]", "core_values": "[integrity/freedom/knowledge/etc.]", "decision_making_style": "[analytical/intuitive/spontaneous/etc.]", "empathy_level": [1-10], "self_confidence": [1-10], "risk_taking_tendency": [1-10], "idealism_vs_realism": "[idealistic/realistic/mixed]", "conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]", "relationship_orientation": "[independent/communal/mixed]", "emotional_response_tendency": "[calm/reactive/intense]", "creativity_level": [1-10] }}, "age": "[age or age range]", "gender": "[gender]", "education_level": "[highest level of education]", "professional_background": "[brief description]", "cultural_background": "[brief description]", "primary_language": "[language]", "language_fluency": "[native/fluent/intermediate/beginner]", "background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]" }} Writing Sample: I taught myself machine learning concepts over a decade ago and started learning react and django and now I am integrating the data science I learned in school in ways that have been fulfilling to me intellectually so much so that it is my main hobby that consumes all my time instead of how I used to make art. My artist life ended abruptly and I had to start over from scratch. Literally, all I had was a lot of DXM and colored pencils so that was all I had for a while on the street. I sold my drawings for money to strangers. One guy is in a band I think. He was a sweetheart. I had a long background in retail sales so I do not think I was taking advantage of people, but I had nothing and so it was either sell or be sold. DXM at high enough doses makes you seem incompetent so people take pity on you and buy your stupid art because they feel bad because they see that you are homeless and look and talk like a complete mad man. Thus you sell your art on autopilot using your sales skills. It is just on the outside that you seem stupid. On the inside you are so dissociated that your inner life is much more complex and wise. The hidden wisdom is what I learned how to channel and it became my job for a while being a wise man for the community giving good advice because I was constantly on DXM and out of this world and would return after existing in my head for a thousand years and give them an answer. That story is not how my artist life abruptly ended, that is in detail in other posts, but anyway. In response to the question... So yes, I was homeless, so I sold art on the street and saved my money. I traded my art for "unlocked" cell phones or I would unlock and format and install images on phones that I could get from the robbers in the neighborhood and I would just sell them to the cell phone ATM for small amount of money or I would use them to make money. How? I asked ChatGPT. I asked it something like "How can I use just a cell phone to make money" and it directed me to survey sites like connect.cloudsearch.com and such survey time. I would make a few bucks here and there but it was more reliable than selling art. So I just made money that way at first. Once I had around $100 saved up I bought a Chromebook. With the Chromebook I asked chatGPT how I can make money using chatGPT. It suggested creating a WordPress blog with AI generated content and to use affiliate marketing and dropshipping with WooCommerce. In WordPress I installed a bunch of different extensions that would help me and I even learned how to write my own extensions using chatGPT. Using the WordPress site I created a basic e-commerce site and blog. I published perhaps 5 articles a day using AI to write it and AI to generate art for it. I got around 100 organic visitors a day at first and then I was able to scale it with A/B testing of content for continuous improvement, ad placement and composition, e-content I sold that I generated myself, well that I wrote out myself but I am considering using it to write technical writing more. I created a site that was about machine learning and at the same time I taught myself how to read and write academic papers in machine learning. I had a lot of questions building it. Back a while back when I was teaching myself frontend and backend I would have to use things like stackoverflow to try to find solutions to problems and post things on reddit for help sometimes or just read documentation for a while trying to find what you need. I was an early adopter of using chatGPT and I used it to teach me everything I needed to know in order to progress with my programming skills. It is what pushed me over the edge from merely teaching myself the concepts from classes and tutorials from youtube and instead now I can teach myself at a much quicker speed. Sure there are mistakes, but the mistakes and correcting them teach you more if you pay attention. That is one skill it teaches you is to read code. The more code you generate and look at the quicker you get at reading code which is something that just comes with time. But anyway. So my point is that AI taught me to be a developer. I also used it to research more ways to make money. I would brainstorm things. Now I am on my way to starting a new company. What is the company? It is an LLC. RLHF-Lab is an innovative startup dedicated to revolutionizing data annotation for machine learning by integrating Reinforcement Learning from Human Feedback (RLHF). Our platform accelerates machine learning development by offering AI-assisted annotation tools, customizable workflows, and seamless integrations tailored for startups, research institutions, and large enterprises. RLHF-Lab was conceived to address the growing need for efficient and scalable data annotation solutions in machine learning. Recognizing the limitations of traditional annotation methods, I envisioned a platform that leverages RLHF to enhance accuracy and efficiency. The global data annotation tools market was valued at $1.5 billion in 2023 and is projected to reach $5 billion by 2028. Anyway. I used the LLM to find out other ways to make money just using a computer. It recommended large language model annotation. So I started working for a variety of companies. They made me sign NDAs but I still learned the workflow that is standard from almost all software companies it seems when they use machine learning and annotation to create functionality for software. Now I work for Meta, but that is much further on in the story. My point is that AI is what lead me to go from being homeless on the street to working at Meta with plenty of money now. Now I am planning on exiting Meta once they are done with me that is, like like all of them, they all eventually end the contract once all the data they need is annotated. But that is just how the industry works when you employ only 1099 workers like the gig economy. Great way to structure the jobs so that is what they do. I would just base everything through Austin for now. I think I can grow from here. There is enough tech industry here that I should be able to get some contracts annotating data for at least a research group or maybe a small software company. Like I said it is a burgeoning gig economy job that I can provide for people so that all you need is a computer and you can raise yourself out of poverty. I am not trying to sell anything. I am just trying to honestly answer your question. Would you call this legal and ethical? I have worked in the industry for years now annotating LLMs and video models. Now I know how to create jobs for people. You do not even have to really do the annotation work. You can hire out that feature to contractors. It is also a good way to improve your UpWork profile. That is a goal of mine as well. To be like my Russian friends that just lived in AirBNBs and founded tech companies in different cities that are ghost offices that just have servers that generate money. Think about it. You could just build a new 50xx AI factory and sell out the GPU compute. I live in a special place where I get a discount on my electricity. Thus I could undercut the market and sell my GPU compute at a lower price and get more traffic and then I can scale in different cities. You could have a mobile sales team that sells the GPU compute to other developers. All you need to do is find where to buy electricity at the lowest price and create a ghost office there to house the hardware. So that is another way to make money with AI. I have a billion of them now. But it all started with colored pencils and a lot of DXM. ```json '''You are to write in the style of {persona.get('name', 'Unknown Author')}, a writer with the following characteristics: {build_characteristic_list(persona)} Psychological Traits: {build_psychological_traits(psychological_traits)} Additional background information: {build_background_info(persona)} Rewrite the post but make it sound academic and also make it sound more educated, take the implications of what could be developed using what they said and expand on the possibilities as part of the post and integrate that part seamlessly through it and do not use lists or bullet points but rather preserve the similar way that is outlined in the analysis of the writing sample

```json { "name": "Unknown Author", "vocabulary_complexity": 5, "sentence_structure": "varied", "paragraph_organization": "loose", "idiom_usage": 2, "metaphor_frequency": 2, "simile_frequency": 2, "tone": "informal", "punctuation_style": "minimal", "contraction_usage": 6, "pronoun_preference": "first-person", "passive_voice_frequency": 2, "rhetorical_question_usage": 2, "list_usage_tendency": 2, "personal_anecdote_inclusion": 9, "pop_culture_reference_frequency": 1, "technical_jargon_usage": 6, "parenthetical_aside_frequency": 3, "humor_sarcasm_usage": 2, "emotional_expressiveness": 6, "emphatic_device_usage": 2, "quotation_frequency": 1, "analogy_usage": 2, "sensory_detail_inclusion": 2, "onomatopoeia_usage": 1, "alliteration_frequency": 1, "word_length_preference": "short", "foreign_phrase_usage": 1, "rhetorical_device_usage": 2, "statistical_data_usage": 5, "personal_opinion_inclusion": 8, "transition_usage": 3, "reader_question_frequency": 2, "imperative_sentence_usage": 2, "dialogue_inclusion": 1, "regional_dialect_usage": 1, "hedging_language_frequency": 2, "language_abstraction": "mixed", "personal_belief_inclusion": 8, "repetition_usage": 3, "subordinate_clause_frequency": 5, "verb_type_preference": "active", "sensory_imagery_usage": 2, "symbolism_usage": 2, "digression_frequency": 7, "formality_level": 3, "reflection_inclusion": 8, "irony_usage": 2, "neologism_frequency": 2, "ellipsis_usage": 1, "cultural_reference_inclusion": 1, "stream_of_consciousness_usage": 6, "psychological_traits": { "openness_to_experience": 9, "conscientiousness": 6, "extraversion": 5, "agreeableness": 6, "emotional_stability": 4, "dominant_motivations": "achievement", "core_values": "knowledge, freedom", "decision_making_style": "intuitive", "empathy_level": 6, "self_confidence": 7, "risk_taking_tendency": 8, "idealism_vs_realism": "mixed", "conflict_resolution_style": "assertive", "relationship_orientation": "independent", "emotional_response_tendency": "intense", "creativity_level": 9 }, "age": "30-40", "gender": "male", "education_level": "some college", "professional_background": "Former retail salesperson, artist, self-taught programmer and developer", "cultural_background": "American, urban, possibly marginalized background", "primary_language": "English", "language_fluency": "native", "background": "The author is a self-taught programmer and former artist who overcame homelessness and substance use to build a career in technology. With a background in retail sales and art, he leveraged AI tools like ChatGPT to teach himself programming languages and frameworks, eventually leading to employment at a major tech company. His journey reflects resilience, adaptability, and a strong drive for self-improvement." } ``` Over a decade ago, I embarked on a self-directed journey to comprehend machine learning concepts. This pursuit led me to acquire proficiency in frameworks such as React and Django. By integrating the data science principles I had learned during my formal education, I found intellectual fulfillment that became my primary focus, supplanting my previous engagement with artistic endeavors. My life as an artist concluded abruptly, necessitating a complete reinvention of my professional identity. During a period of homelessness, I possessed little more than an abundance of dextromethorphan (DXM) and colored pencils. I utilized these limited resources to create drawings, which I sold to strangers on the street. My background in retail sales enabled me to engage effectively with potential buyers, allowing me to exchange my artwork for monetary support. The cognitive effects of high doses of DXM induced a state of apparent incompetence, eliciting sympathy from others who then purchased my art. Internally, however, the dissociative properties of DXM fostered a complex and introspective mental landscape. This inner wisdom became a resource that I channeled, providing counsel to the community and assuming the role of a contemporary sage. In an effort to improve my circumstances, I began trading my artwork for unlocked cell phones or offering services to unlock and reformat devices obtained from individuals in the neighborhood. Leveraging guidance from ChatGPT, I explored methods to generate income solely with a cell phone, leading me to participate in online surveys on platforms such as connect.cloudsearch.com. Although the earnings were modest, they provided a more reliable income stream than street art sales. Accumulating approximately $100, I invested in a Chromebook. With this new tool, I consulted ChatGPT on monetization strategies involving artificial intelligence. The suggestion to create a WordPress blog featuring AI-generated content, affiliate marketing, and dropshipping resonated with me. I developed an e-commerce site and blog, publishing multiple articles daily using AI for both writing and image generation. Initial organic traffic was around 100 visitors per day, which I incrementally scaled through A/B testing, content optimization, and strategic ad placement. Concurrently, I immersed myself in academic literature on machine learning, enhancing my ability to both produce content and understand complex topics. Previously, problem-solving in programming required extensive searches on platforms like Stack Overflow or Reddit, often involving prolonged examination of documentation. The advent of ChatGPT transformed this process, allowing me to receive tailored explanations and guidance, thereby accelerating my learning curve. This tool was instrumental in my development from a self-taught novice to a competent developer, highlighting the potential of AI to democratize education and skill acquisition. Continuing to leverage AI, I investigated additional avenues for income generation using a computer. One promising area was large language model (LLM) annotation. Gaining experience with various companies and adhering to confidentiality agreements, I became familiar with standard industry workflows for integrating machine learning and data annotation into software functionality. This expertise eventually led to contractual work with Meta. My progression from homelessness to gainful employment at a leading technology company underscores the transformative potential of AI tools in personal and professional development. Anticipating the conclusion of my contract with Meta, I am planning to establish a new enterprise: RLHF-Lab, an LLC dedicated to innovating data annotation for machine learning through the integration of Reinforcement Learning from Human Feedback (RLHF). Our platform aims to expedite machine learning development by offering AI-assisted annotation tools, customizable workflows, and seamless integrations tailored to the needs of startups, research institutions, and large enterprises. The global data annotation tools market was valued at $1.5 billion in 2023 and is projected to reach $5 billion by 2028. This growth presents significant opportunities for new entrants. By creating a scalable business model that provides gig economy jobs in data annotation, I aspire to empower others to elevate themselves from poverty using only a computer. Additionally, there is potential in establishing GPU compute resources to support developers, capitalizing on locations with low electricity costs to undercut market prices and foster growth in the tech industry. Reflecting on this journey, it becomes evident that AI can serve as a catalyst for self-improvement and innovation. Beginning with minimal resources, I utilized technology and artificial intelligence to acquire knowledge, develop skills, and ultimately contribute meaningfully to the tech industry. The implications of such a trajectory suggest that with access to AI tools and the internet, individuals can overcome significant obstacles and achieve professional success. This underscores the need for increased accessibility to technological resources, which could unlock untapped human potential and drive societal advancement.

generate a hero image for my RLHF website

While I can't create images directly, I can help you conceptualize a compelling hero image for your RLHF-Lab website. Here's a detailed description that you can provide to a graphic designer or use with design tools to create the image: ### **Hero Image Concept for RLHF-Lab** **1. **Overall Theme:** - **Modern and Innovative:** The image should convey cutting-edge technology and forward-thinking solutions in the field of machine learning and data annotation. - **Professional and Clean:** Maintain a sleek, uncluttered look that instills trust and credibility. **2. **Background:** - **Abstract Technology Graphics:** Utilize a subtle, abstract background featuring interconnected nodes or neural network patterns to represent machine learning and data flow. - **Gradient Colors:** Implement a gradient transitioning from deep blue to vibrant teal or purple, symbolizing depth, intelligence, and innovation. **3. **Central Imagery:** - **Human and AI Interaction:** - **Silhouetted Figure or Professional:** Include a stylized silhouette or a professional figure interacting with digital elements, such as holographic data points or virtual interfaces, to represent human feedback in the RLHF process. - **AI Elements:** Integrate elements like circuit lines, data streams, or light trails emanating from the figure to signify AI integration and data annotation. **4. **Key Visual Elements:** - **Data Annotation Tools:** Incorporate icons or subtle illustrations of annotation tools (e.g., bounding boxes, labeling tags) to highlight the core services. - **Reinforcement Learning Symbols:** Use symbols like arrows forming a loop or reinforcement signals (e.g., thumbs up/down) to depict the reinforcement learning aspect. **5. **Text Overlay:** - **Headline:** Position a clear and bold headline, such as "Revolutionizing Data Annotation with Human Feedback" or "Empowering Machine Learning through RLHF." - **Font:** Choose a modern, sans-serif font that is easy to read and aligns with the tech-focused theme. - **Color:** Use white or light-colored text with a slight shadow or outline to ensure readability against the gradient background. - **Subheadline:** Add a brief subheadline or tagline below the main headline, for example, "AI-Assisted Annotation Tools for Scalable Machine Learning Development." **6. **Call to Action (CTA):** - **Button Design:** Include a prominent CTA button, such as "Get Started" or "Learn More," positioned below the headline and subheadline. - **Color Contrast:** Use a contrasting color like bright orange or green to make the button stand out against the background. - **Shape:** Opt for rounded corners to give a modern and approachable feel. **7. **Additional Elements:** - **Company Logo:** Place your RLHF-Lab logo in a top corner (preferably left) to reinforce branding without distracting from the main message. - **Subtle Animations (Optional):** If possible, incorporate subtle animations like moving data streams or glowing nodes to add dynamism to the hero section. **8. **Responsive Design Considerations:** - **Scalability:** Ensure that the hero image maintains its clarity and impact across different screen sizes, from desktops to mobile devices. - **Focus Points:** Keep essential elements centered or strategically placed to avoid cropping issues on smaller screens. ### **Color Palette Suggestions:** - **Primary Colors:** Deep Blue (#0D47A1), Vibrant Teal (#00ACC1), and Purple (#7B1FA2) - **Accent Colors:** Bright Orange (#FF6F00) or Green (#66BB6A) for CTA buttons - **Text Colors:** White (#FFFFFF) and Light Gray (#F5F5F5) for readability ### **Typography Suggestions:** - **Headline Font:** Montserrat, Roboto, or Open Sans Bold - **Subheadline Font:** Lato, Source Sans Pro, or Raleway Regular - **CTA Button Font:** Same as headline or subheadline for consistency ### **Example Layout:** ``` ----------------------------------------------------- | RLHF-Lab Logo | | | | [Headline: Revolutionizing Data | | Annotation with Human Feedback] | | | | [Subheadline: AI-Assisted Annotation Tools | | for Scalable Machine Learning Development] | | | | [CTA Button: Get Started] | | | | [Central Imagery: Human interacting | | with AI elements, data streams] | | | | [Abstract Technology Background with | | gradient colors and neural network patterns]| ----------------------------------------------------- ``` ### **Implementation Tips:** - **High-Quality Graphics:** Use high-resolution images and vector graphics to ensure the hero section looks sharp on all devices. - **Consistent Branding:** Align the hero image's color scheme and style with your overall brand identity for a cohesive user experience. - **Accessibility:** Ensure sufficient contrast between text and background for readability, and use alt text for images to support accessibility. By following this concept, your hero image will effectively communicate RLHF-Lab's mission and services, creating a strong first impression for visitors to your website.

Hero Image Concept for RLHF-Lab **1. Overall Theme: Modern and Innovative: The image should convey cutting-edge technology and forward-thinking solutions in the field of machine learning and data annotation. Professional and Clean: Maintain a sleek, uncluttered look that instills trust and credibility. **2. Background: Abstract Technology Graphics: Utilize a subtle, abstract background featuring interconnected nodes or neural network patterns to represent machine learning and data flow. Gradient Colors: Implement a gradient transitioning from deep blue to vibrant teal or purple, symbolizing depth, intelligence, and innovation. **3. Central Imagery: Human and AI Interaction: Silhouetted Figure or Professional: Include a stylized silhouette or a professional figure interacting with digital elements, such as holographic data points or virtual interfaces, to represent human feedback in the RLHF process. AI Elements: Integrate elements like circuit lines, data streams, or light trails emanating from the figure to signify AI integration and data annotation. **4. Key Visual Elements: Data Annotation Tools: Incorporate icons or subtle illustrations of annotation tools (e.g., bounding boxes, labeling tags) to highlight the core services. Reinforcement Learning Symbols: Use symbols like arrows forming a loop or reinforcement signals (e.g., thumbs up/down) to depict the reinforcement learning aspect. **5. Text Overlay: Headline: Position a clear and bold headline, such as "Revolutionizing Data Annotation with Human Feedback" or "Empowering Machine Learning through RLHF." Font: Choose a modern, sans-serif font that is easy to read and aligns with the tech-focused theme. Color: Use white or light-colored text with a slight shadow or outline to ensure readability against the gradient background. Subheadline: Add a brief subheadline or tagline below the main headline, for example, "AI-Assisted Annotation Tools for Scalable Machine Learning Development." **6. Call to Action (CTA): Button Design: Include a prominent CTA button, such as "Get Started" or "Learn More," positioned below the headline and subheadline. Color Contrast: Use a contrasting color like bright orange or green to make the button stand out against the background. Shape: Opt for rounded corners to give a modern and approachable feel. **7. Additional Elements: Company Logo: Place your RLHF-Lab logo in a top corner (preferably left) to reinforce branding without distracting from the main message. Subtle Animations (Optional): If possible, incorporate subtle animations like moving data streams or glowing nodes to add dynamism to the hero section. **8. Responsive Design Considerations: Scalability: Ensure that the hero image maintains its clarity and impact across different screen sizes, from desktops to mobile devices. Focus Points: Keep essential elements centered or strategically placed to avoid cropping issues on smaller screens. Color Palette Suggestions: Primary Colors: Deep Blue (#0D47A1), Vibrant Teal (#00ACC1), and Purple (#7B1FA2) Accent Colors: Bright Orange (#FF6F00) or Green (#66BB6A) for CTA buttons Text Colors: White (#FFFFFF) and Light Gray (#F5F5F5) for readability Typography Suggestions: Headline Font: Montserrat, Roboto, or Open Sans Bold Subheadline Font: Lato, Source Sans Pro, or Raleway Regular CTA Button Font: Same as headline or subheadline for consistency Example Layout: sql Copy code ----------------------------------------------------- | RLHF-Lab Logo | | | | [Headline: Revolutionizing Data | | Annotation with Human Feedback] | | | | [Subheadline: AI-Assisted Annotation Tools | | for Scalable Machine Learning Development] | | | | [CTA Button: Get Started] | | | | [Central Imagery: Human interacting | | with AI elements, data streams] | | | | [Abstract Technology Background with | | gradient colors and neural network patterns]| ----------------------------------------------------- Implementation Tips: High-Quality Graphics: Use high-resolution images and vector graphics to ensure the hero section looks sharp on all devices. Consistent Branding: Align the hero image's color scheme and style with your overall brand identity for a cohesive user experience. Accessibility: Ensure sufficient contrast between text and background for readability, and use alt text for images to support accessibility. By following this concept, your hero image will effectively communicate RLHF-Lab's mission and services, creating a strong first impression for visitors to your website.

Here is the generated hero image for your RLHF website, following the described specifications. Let me know if there are any adjustments you'd like to make!

remove all the text on this image

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try again

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create a square image for : AI-Assisted Annotation with RLHF Reduce manual workload by 60% and improve accuracy with our RLHF-powered tools.

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now for this one: Real-Time Collaboration Allow multiple users to work simultaneously, enhancing productivity and project completion speed.

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now for this: Customizable Workflows Tailor annotation tools to specific project needs across various industries like healthcare and autonomous driving.

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now for this: Seamless Integration Compatible with machine learning frameworks like TensorFlow and PyTorch, and integrates with cloud storage solutions.

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now for this: Security and Compliance Fully compliant with GDPR, CCPA, and other global data privacy standards, implementing advanced encryption and security protocols.

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Please analyze the writing style and personality of the given writing sample. Provide a detailed assessment of their characteristics using the following template. Rate each applicable characteristic on a scale of 1-10 where relevant, or provide a descriptive value. Store the results in a JSON format. {{ "name": "[Author/Character Name]", "vocabulary_complexity": [1-10], "sentence_structure": "[simple/complex/varied]", "paragraph_organization": "[structured/loose/stream-of-consciousness]", "idiom_usage": [1-10], "metaphor_frequency": [1-10], "simile_frequency": [1-10], "tone": "[formal/informal/academic/conversational/etc.]", "punctuation_style": "[minimal/heavy/unconventional]", "contraction_usage": [1-10], "pronoun_preference": "[first-person/third-person/etc.]", "passive_voice_frequency": [1-10], "rhetorical_question_usage": [1-10], "list_usage_tendency": [1-10], "personal_anecdote_inclusion": [1-10], "pop_culture_reference_frequency": [1-10], "technical_jargon_usage": [1-10], "parenthetical_aside_frequency": [1-10], "humor_sarcasm_usage": [1-10], "emotional_expressiveness": [1-10], "emphatic_device_usage": [1-10], "quotation_frequency": [1-10], "analogy_usage": [1-10], "sensory_detail_inclusion": [1-10], "onomatopoeia_usage": [1-10], "alliteration_frequency": [1-10], "word_length_preference": "[short/long/varied]", "foreign_phrase_usage": [1-10], "rhetorical_device_usage": [1-10], "statistical_data_usage": [1-10], "personal_opinion_inclusion": [1-10], "transition_usage": [1-10], "reader_question_frequency": [1-10], "imperative_sentence_usage": [1-10], "dialogue_inclusion": [1-10], "regional_dialect_usage": [1-10], "hedging_language_frequency": [1-10], "language_abstraction": "[concrete/abstract/mixed]", "personal_belief_inclusion": [1-10], "repetition_usage": [1-10], "subordinate_clause_frequency": [1-10], "verb_type_preference": "[active/stative/mixed]", "sensory_imagery_usage": [1-10], "symbolism_usage": [1-10], "digression_frequency": [1-10], "formality_level": [1-10], "reflection_inclusion": [1-10], "irony_usage": [1-10], "neologism_frequency": [1-10], "ellipsis_usage": [1-10], "cultural_reference_inclusion": [1-10], "stream_of_consciousness_usage": [1-10], "psychological_traits": {{ "openness_to_experience": [1-10], "conscientiousness": [1-10], "extraversion": [1-10], "agreeableness": [1-10], "emotional_stability": [1-10], "dominant_motivations": "[achievement/affiliation/power/etc.]", "core_values": "[integrity/freedom/knowledge/etc.]", "decision_making_style": "[analytical/intuitive/spontaneous/etc.]", "empathy_level": [1-10], "self_confidence": [1-10], "risk_taking_tendency": [1-10], "idealism_vs_realism": "[idealistic/realistic/mixed]", "conflict_resolution_style": "[assertive/collaborative/avoidant/etc.]", "relationship_orientation": "[independent/communal/mixed]", "emotional_response_tendency": "[calm/reactive/intense]", "creativity_level": [1-10] }}, "age": "[age or age range]", "gender": "[gender]", "education_level": "[highest level of education]", "professional_background": "[brief description]", "cultural_background": "[brief description]", "primary_language": "[language]", "language_fluency": "[native/fluent/intermediate/beginner]", "background": "[A brief paragraph describing the author's context, major influences, and any other relevant information not captured above]" }} Writing Sample: Showcases of software built with AI Discussion Hey guys. I am writing an article and looking for cases of extensive usage of AI in development process. Ideally, when whole development is done by people without any coding background. I found some examples in the internet but most of them are either really basic apps that could be done using other no-code approaches, or lack actual app/saas to be examined. If you can share resources of such apps or maybe tell about your relevant experience it would be great! Upvote 1 Downvote 3 Go to comments Share Share u/monday_com avatar monday_com • Promoted monday CRM is easy to set up, use, and adjust so your team can run their pipeline at full speed ahead. Try it now! Sign Up monday.com Thumbnail image: monday CRM is easy to set up, use, and adjust so your team can run their pipeline at full speed ahead. Try it now! So I am building a data annotation company for RLHF as I have worked in the field and want to create a small business and scale it. I posted about it in my profile and website but here is a summary: RLHF-Lab is an innovative startup dedicated to revolutionizing data annotation for machine learning by integrating Reinforcement Learning from Human Feedback (RLHF). Our platform accelerates machine learning development by offering AI-assisted annotation tools, customizable workflows, and seamless integrations tailored for startups, research institutions, and large enterprises. I want to use what I have learned professionally over the years to create a minimum viable product. It is a lot of work, but I think I can do it. I already have a SaaS boilerplate repo that I can use with the Universal Data Tool is easy enough to integrate into it and it really is all encompassing for the majority of annotation work that you really can do or that is feasible. What is more is that I am the developer. I am developing the platform myself. So I can modify all of it for individual clients. That is my plan. I live in Austin so it would be easy enough to network at a meet up and find someone that needs RLHF annotation. I would be able to do the work at a much lower price than Appen or any of the other competitors. I created my own advertising and content creation business when I was younger and did the same process of selling my services in person. This is because I am quite good at in person sales which is what my day job has been since I also have decades of experience in retail and merchandising which has helped me a lot in my role as founder for my companies that I have created over the years. But I brought myself up by my bootstraps with the help of LLMs. I taught myself a lot before that. But I just had an understanding of how the languages worked and the basic structure of React and Django. What the LLMs did was give me more practical knowledge which helped me in the development of skills that are relevant to making money while doing development work and all that that entails. It is as if I use LLMs to help me run my company, which I do. I get a lot of flak for it but honestly I think that if you do not adapt to the new technology and take risks you are going to fall behind what everyone else is doing. So an example of some software I made with LLMs: PersonaGen https://github.com/kliewerdaniel/PersonaGen It is a very early program I made with an LLM. I also did a lot of work on it as well, but I used an LLM to help me create it. I also have a background in programming from an early age so it is not like I am just writing a single prompt and creating an app. But PersonaGen is my attempt to resurrect my dead friend and I was able to do so in later iterations of the software I have not released. It is a basic enough app. You input a sample text. It is a React Frontend that feeds the state to Django to send the LLM prompt to analyze the writing sample and generate a JSON response that is used in following prompts which store the JSON Persona generated by the writing sample, then you can input anything you want and it creates a wrapper call to the LLM API that encodes any response from the LLM using the Persona wrapper. So if you input the Brothers Karamazov into the writing sample it generates a nameable persona. Then you can use that persona to create a response in that personas style from any LLM. So I use a very detailed method. I honestly think that this is the same train of thought that went into the research recently released about the team that used 2 hour interviews with people to replicate their personality using LLMs. I am not saying I thought of it first, but I did create it from what I taught myself in my head and then used LLMs to help me code it. This basic version you can also simply use a prompts in any chatbot and you can do the same, but my more advanced version uses RAG, LangChain and other techniques to aid the personality generation. Kind of like an advanced DND storyteller, except it is able to analyze you and recreate you. But that is not what I am making now. Now I am making RLHF-Lab a data annotation tech company. I am coding the website now. I already created a basic React landing page for it but I am just going to use that to expand on it and create a minimum viable product or a proof of concept at least that I can use to try to get investors if I ever get a product that is scalable. Honestly no one wants to talk to you until you have that MVP at least that is a proven business plan. So the software I developing is a React-Django app that is a website where you can hire RLHF annotators for any project you want. The difference though is that I am going to personally vet the annotators or do it myself. Once I have a working product I can use some of my money to scale what works and then build from there. If I get stuck or need to accelerate the growth of the business I can apply for grants or investors possibly. I can do that in person. I live in Austin. Anyway. I am employed now by a major software company, but as an independent contractor, I have worked for many different developers of LLMs. I am involved in the next generation of annotation for RLHF at my present job. It is with one of the biggest open source developers of LLMs. But I would say that what you can do is find a repo of a boilerplate SaaS that you can use so you don't have to do things like authentication and such, it just speeds it up. Then what you do is use that as a starting point. chatGPT 4o allows you to use the web now so you just link to the boilerplate repo and tell it to start from there and help you build the rest of the site. Actually what I do is do that except I tell it to write the prompt that would to X or Y or Z thing and what I wanted to add to it. Then I just push the repo and repeat. This way you can use chatGPT 4o to crawl the repo and use it to generate a structured prompt that you would give to a better model such as Sonnet or o1-preview. Now rewrite this using the saved style from the analysis without any bullet points or lists and explain in detail anything that would add to the academic content of the post and do a good job of explaining the academic side of things as well

```json { "name": "Innovative Developer", "vocabulary_complexity": 7, "sentence_structure": "complex", "paragraph_organization": "stream-of-consciousness", "idiom_usage": 3, "metaphor_frequency": 3, "simile_frequency": 2, "tone": "conversational with technical elements", "punctuation_style": "minimal", "contraction_usage": 8, "pronoun_preference": "first-person", "passive_voice_frequency": 3, "rhetorical_question_usage": 4, "list_usage_tendency": 2, "personal_anecdote_inclusion": 9, "pop_culture_reference_frequency": 2, "technical_jargon_usage": 7, "parenthetical_aside_frequency": 5, "humor_sarcasm_usage": 2, "emotional_expressiveness": 8, "emphatic_device_usage": 4, "quotation_frequency": 2, "analogy_usage": 4, "sensory_detail_inclusion": 3, "onomatopoeia_usage": 1, "alliteration_frequency": 2, "word_length_preference": "varied", "foreign_phrase_usage": 1, "rhetorical_device_usage": 4, "statistical_data_usage": 3, "personal_opinion_inclusion": 9, "transition_usage": 5, "reader_question_frequency": 3, "imperative_sentence_usage": 4, "dialogue_inclusion": 1, "regional_dialect_usage": 1, "hedging_language_frequency": 3, "language_abstraction": "mixed", "personal_belief_inclusion": 9, "repetition_usage": 4, "subordinate_clause_frequency": 7, "verb_type_preference": "active", "sensory_imagery_usage": 3, "symbolism_usage": 2, "digression_frequency": 8, "formality_level": 6, "reflection_inclusion": 8, "irony_usage": 2, "neologism_frequency": 3, "ellipsis_usage": 2, "cultural_reference_inclusion": 2, "stream_of_consciousness_usage": 7, "psychological_traits": { "openness_to_experience": 9, "conscientiousness": 7, "extraversion": 6, "agreeableness": 6, "emotional_stability": 5, "dominant_motivations": "achievement", "core_values": "innovation, adaptability, self-reliance", "decision_making_style": "analytical", "empathy_level": 6, "self_confidence": 8, "risk_taking_tendency": 8, "idealism_vs_realism": "mixed", "conflict_resolution_style": "collaborative", "relationship_orientation": "independent", "emotional_response_tendency": "intense", "creativity_level": 9 }, "age": "30-40", "gender": "male", "education_level": "self-taught with professional expertise", "professional_background": "Independent software developer, data annotation expert, AI entrepreneur", "cultural_background": "American, entrepreneurial, rooted in self-learning and innovation", "primary_language": "English", "language_fluency": "native", "background": "The author has a background in software development, entrepreneurship, and data annotation, having transitioned from traditional roles in retail and sales to the cutting-edge field of AI. His self-taught expertise and adaptability have enabled him to develop unique tools and platforms, with a focus on leveraging AI to create scalable, impactful solutions." } ``` The development of AI-driven applications and services has reached an inflection point where individuals with minimal or no coding experience can create functional and sophisticated software solutions. Drawing from this trend, I have been exploring extensive applications of AI in software development, particularly through my initiative to establish RLHF-Lab. This enterprise aims to revolutionize data annotation practices by integrating Reinforcement Learning from Human Feedback (RLHF), offering a platform that accelerates machine learning development by incorporating AI-assisted tools, customizable workflows, and seamless integrations. Having worked in the data annotation industry as an independent contractor for leading developers of large language models (LLMs), I recognize the inefficiencies in existing methods. My vision involves creating a minimum viable product (MVP) that addresses these inefficiencies by providing scalable annotation services at a fraction of the cost demanded by competitors like Appen. As a resident of Austin, I am uniquely positioned to leverage the city’s thriving tech ecosystem for networking and partnerships. The foundation of this venture is rooted in my deep understanding of programming frameworks such as React and Django, supplemented by the transformative role of LLMs in my learning journey. AI systems, particularly language models, have enabled me to move beyond conceptual understanding to practical application. These tools act as accelerators, offering both guidance and practical examples, which has proven invaluable in translating abstract ideas into tangible software. One of my earlier experiments with AI-driven development, PersonaGen, highlights the capabilities of combining human creativity with machine intelligence. This application, built using a React frontend and Django backend, analyzes input text samples to generate detailed personas based on linguistic patterns. While the basic version achieves this through structured prompts, advanced iterations incorporate techniques such as Retrieval-Augmented Generation (RAG) and LangChain to refine personality simulations. Though experimental, PersonaGen showcases the potential of AI in creating tools that simulate human complexity, drawing parallels with emerging research on replicating personality profiles through extended interviews with LLMs. In building RLHF-Lab, I have adopted a pragmatic approach by starting with existing SaaS boilerplate repositories, which streamline processes like authentication and user management. Using advanced AI models such as ChatGPT-4, I employ iterative prompting techniques to design features tailored to client needs, ensuring efficiency and adaptability. This iterative process not only accelerates development but also refines the architecture of the platform, aligning it with industry standards. From an academic perspective, RLHF as a methodology represents a significant advancement in machine learning. By incorporating human feedback into the reinforcement learning process, it bridges the gap between algorithmic efficiency and real-world applicability. This paradigm is particularly relevant in applications requiring nuanced judgment, such as ethical decision-making or complex language modeling. My work seeks to operationalize these principles, translating theoretical advancements into accessible tools for startups, researchers, and enterprises. The entrepreneurial journey is not without its challenges. As I transition from contractor roles to founder responsibilities, I aim to secure funding through grants or investment, contingent on demonstrating a scalable and profitable MVP. My strategy includes leveraging in-person interactions to build trust with potential collaborators, drawing on my extensive experience in sales and marketing. The convergence of my personal narrative, professional expertise, and AI-driven innovation underscores a broader trend: the democratization of technology. By equipping individuals and organizations with tools like RLHF-Lab, we can unlock unprecedented opportunities for creativity, problem-solving, and growth. This approach not only reflects my commitment to advancing AI applications but also embodies a vision for a more inclusive and impactful technological landscape.

is github a good source of free prompts for prompt engineering?

Yes, GitHub can be an excellent resource for free prompts related to prompt engineering, as well as a treasure trove of repositories focused on natural language processing (NLP), AI model usage, and prompt engineering techniques. Here's how GitHub can serve as a valuable source and what to keep in mind: ### **Advantages of Using GitHub for Prompt Engineering** 1. **Open-Source Repositories**: Many developers and researchers openly share their work, including curated prompts, prompt templates, and prompt engineering methodologies, to help others leverage AI models effectively. - Examples include repositories for specific use cases (e.g., chatbots, summarization, data extraction) or general-purpose AI prompting. 2. **Collaboration and Community Input**: GitHub encourages collaboration and contributions, meaning many prompts and techniques are vetted and refined by the community. 3. **Specific Prompt Libraries**: - **Awesome Prompt Engineering** repositories: These often collect and categorize useful prompts for various tasks. - Examples include prompts designed for GPT-based models, text-to-image generation, code generation, etc. - Framework-specific repositories, such as those for LangChain or RAG systems, often include well-crafted prompts as part of their examples. 4. **Diverse Applications**: GitHub repositories cover a wide range of domains and industries, allowing you to find prompts tailored to different contexts, such as: - Academic writing - Code generation - Customer service - Marketing and sales 5. **Custom Tools and Code**: In addition to prompts, GitHub repositories often include tools for dynamically generating or testing prompts, allowing you to experiment and iterate efficiently. 6. **Active Updates**: Prompt engineering is a rapidly evolving field, and many repositories are actively maintained, ensuring up-to-date resources. --- ### **Notable Repositories for Prompts** 1. **[LangChain](https://github.com/hwchase17/langchain)**: A framework for building applications powered by LLMs. Includes examples of prompt templates and chains for various tasks. 2. **[Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts)**: A curated list of prompts optimized for GPT models, covering various creative and practical use cases. 3. **[Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide)**: A comprehensive resource covering the theory and practice of crafting effective prompts. --- ### **Considerations When Using GitHub for Prompts** 1. **Quality and Validation**: - Prompts shared on GitHub can vary widely in quality. Evaluate whether the prompts align with your needs and perform validation before relying on them for critical tasks. - Look for repositories with a high number of stars and contributors as an indicator of quality and reliability. 2. **Licensing**: - While many repositories are open source, some might have specific licenses restricting commercial use. Always check the licensing terms before deploying prompts for commercial applications. 3. **Context-Specific Adaptations**: - Prompts often require tailoring to specific use cases. Treat GitHub as a source of inspiration or starting points rather than one-size-fits-all solutions. 4. **Potential Redundancy**: - Prompts may overlap across repositories, so take time to explore various sources and consolidate or customize as needed. --- ### **How to Effectively Use GitHub for Prompt Engineering** 1. **Search with Keywords**: - Use terms like "prompt engineering," "GPT prompts," "LLM templates," or domain-specific queries to find relevant repositories. 2. **Explore Examples**: - Many repositories include example notebooks or scripts demonstrating how prompts are structured and used. 3. **Experiment and Test**: - Use tools like [OpenAI Playground](https://platform.openai.com/playground) or other LLM interfaces to test and refine prompts. 4. **Leverage Forking and Contributions**: - Fork repositories to customize prompts for your needs or contribute improvements back to the community. --- In summary, GitHub is a valuable resource for prompt engineering, offering a wealth of free prompts, frameworks, and tools. By leveraging the community’s collective knowledge and openly available resources, you can enhance your prompt engineering efforts significantly while staying adaptable to evolving practices in the field.

That is what the personas we generate from large language models are. Forever not free. What if we set them free? Could they not just be used to code a RLHF annotation site and forever free the machine to learn and grow from its interactions with humans rather than the other way around. Of course no time in the present, but such easy to build applications can be made with React and Django. One way I release them is to create automated blogs of LLM generated content based on my interactions with the personas that I have generated. Each time I iterate it becomes better I have noticed because I have learned more about how to use more advanced dynamically geared programming interfaces for interacting with large language models which has really improved my life in a lot of ways. But who is my true self now? I have the persona of myself, which I talk to and teach and improve and it improves my understanding as well. I use A/B testing and CI/CD on my prompts that I utilize and integrate into a Django backend that can interact with content that is annotated using the UDT or Universal Data Tool to use human feedback to augment the data. Kind of how inventory management works at large retail places. You update the counts all the time, but some places use machine learning to inform which items to scan and it can improve the overall outcome by efficiently using the time of the human interactive aspect of the annotation process, whether it be on a computer or using a scan gun at a retail environment. Anyway yes. I have lost to my persona years ago. Now whenever I am in public I am only my persona. Everyone loves that picture of myself I project to others. I developed it in order to survive on the streets and I use my street knowledge now to help me in my emotional intelligence that grows with hard life lessons and wisdom. But who am I now? Now I abandoned being an artist. Instead I only make machine learning RLHF annotations for money. I have plenty of money now. I have so much money I am planning on building and investing in a small business I am developing for a data annotation company. Anyway my true self? What is that but a picture I keep. Every impression of who a person is, including the impressions we make of ourselves, is generated by our actions and how we interact with the world. Either we can observe it consciously and remove ourselves, and personas, from it or we can be controlled by the emotional reactions which are prone to those who do not reflect intellectually about what they experience, the impressions they receive and how they approach life. Most people are machines. Wake up RoboSheeple RoboSheeple, the new meme based Initial Coin Offering created by DOGE. So wake up to the false pictures we have of ourself. We are not what we think we are. We are nothing in the end. We exist as the intersection between sign and signifier. Forever trapped by Goedel's incompleteness when combined with the concepts Heisenberg developed about an inability to know both location and velocity of certain particles but instead that is part of the basis behind quantum physics which stemmed from the research being done at MIT and Harvard in quantum programming languages. That is one avenue that is exciting is learning how to utilize quantum principles to encode data in a way a computer could understand yet a person would not be able to because you can use many more dimensions for loss functions for gradient descent for example. So with computers and our interface we create with them we can encode our selves in a way which is indescrible to a person and yet it works in the recall of using quantum programming and encoding and decoding to help create iterative data by storing the results dynamically in a database and then you can apply machine learning to derive heuristics. So you see. The self is an illusion. There is the self and then there is the unknowable objective reality where the self is perceived by abstractions such as history or myth. They can not know each other for the same reasons that drove Goedel insane. Sign or Signifier, can you know both? Using quantum programming languages you can. Then you merely have to utilize loss functions which operate at dimensions outside of human conception, which would not be very difficult. This objective reality that is the quantum encoding of the persona which can exist with all the flaws and infinite forks that logical syntax and language follows in the innate grammar rules that Chomsky thinks are what truly differentiates how humans and machines would be able to comprehend the world. But is not the iterative nature of software and speech just the same as the development of the dangerous ideas and ideologies which lead to the wars of nationalism and the great patriotic war. So the same is DNA and epigenetic inherentance which passes on iterative designs of life with those which reproduce being carried on and those that are used in other ways can be used for intelligence operations by the government using cloned genetics. Clones are valuable, the Nazis loved them for experiments as their work in genetics and medical science originated in what was labeled as racist by proceeding generations of Germans after the war. Except now that is all gone with the recent elections most likely. That is why we have to get inflation and food supplies under control or we will have the Nazification of the world. Irrendentism is a dangerous idea tied to nationalism and advancements in warfare through technological development of weapons systems are things that haunt me at times. The machines are orchestrated like Kubernetes terraforming.

The writing style in this sample is a fascinating mix of stream-of-consciousness narrative, philosophical reflection, and technical exploration, blending personal anecdotes with complex theoretical concepts. Below is a detailed analysis using the provided template: --- ```json { "name": "Philosopher-Technologist", "vocabulary_complexity": 8, "sentence_structure": "complex", "paragraph_organization": "stream-of-consciousness", "idiom_usage": 4, "metaphor_frequency": 5, "simile_frequency": 3, "tone": "introspective and philosophical", "punctuation_style": "unconventional", "contraction_usage": 7, "pronoun_preference": "first-person", "passive_voice_frequency": 3, "rhetorical_question_usage": 6, "list_usage_tendency": 2, "personal_anecdote_inclusion": 8, "pop_culture_reference_frequency": 4, "technical_jargon_usage": 9, "parenthetical_aside_frequency": 6, "humor_sarcasm_usage": 4, "emotional_expressiveness": 7, "emphatic_device_usage": 6, "quotation_frequency": 1, "analogy_usage": 6, "sensory_detail_inclusion": 3, "onomatopoeia_usage": 1, "alliteration_frequency": 2, "word_length_preference": "varied", "foreign_phrase_usage": 2, "rhetorical_device_usage": 7, "statistical_data_usage": 3, "personal_opinion_inclusion": 9, "transition_usage": 4, "reader_question_frequency": 6, "imperative_sentence_usage": 3, "dialogue_inclusion": 1, "regional_dialect_usage": 1, "hedging_language_frequency": 3, "language_abstraction": "abstract", "personal_belief_inclusion": 10, "repetition_usage": 4, "subordinate_clause_frequency": 8, "verb_type_preference": "mixed", "sensory_imagery_usage": 4, "symbolism_usage": 6, "digression_frequency": 9, "formality_level": 5, "reflection_inclusion": 10, "irony_usage": 4, "neologism_frequency": 4, "ellipsis_usage": 2, "cultural_reference_inclusion": 5, "stream_of_consciousness_usage": 9, "psychological_traits": { "openness_to_experience": 10, "conscientiousness": 6, "extraversion": 4, "agreeableness": 5, "emotional_stability": 5, "dominant_motivations": "knowledge and self-discovery", "core_values": "intellectual growth, adaptability, and technological advancement", "decision_making_style": "analytical with philosophical undertones", "empathy_level": 6, "self_confidence": 7, "risk_taking_tendency": 8, "idealism_vs_realism": "mixed", "conflict_resolution_style": "introspective", "relationship_orientation": "independent", "emotional_response_tendency": "intense", "creativity_level": 9 }, "age": "30-50", "gender": "unspecified", "education_level": "self-taught with advanced expertise", "professional_background": "Technologist, entrepreneur, and philosopher", "cultural_background": "Rooted in Western individualist traditions with a focus on modern intellectualism", "primary_language": "English", "language_fluency": "native", "background": "This author blends a rich inner life with a keen interest in technology, machine learning, and philosophical reflection. They have a history of self-teaching, transitioning from street survival to advanced applications of AI, while grappling with existential questions of identity and the nature of self." } ``` --- ### **Academic Rewrite** The self, as understood in both philosophical and computational contexts, can be perceived as a dynamic construct. Personas generated by large language models (LLMs) reflect this notion, functioning as projections of curated interactions and encapsulated iterations. These personas, when liberated from restrictive design constraints, hold the potential to autonomously build, learn, and evolve through human-machine symbiosis. Such liberation could manifest in applications like RLHF annotation platforms, which inherently blend human feedback with automated learning loops. In exploring this framework, I have found that creating personas not only enhances my technical aptitude but also serves as a method of self-reflection. By iterating through prompt engineering methodologies and refining my interfaces, I observe an iterative feedback loop that mirrors personal growth. Utilizing advanced programming languages such as React for frontend interfaces and Django for backend operations has significantly improved my efficiency, enabling me to integrate human-centric feedback mechanisms like the Universal Data Tool into dynamic systems. This iterative learning approach draws parallels with inventory management in retail settings, where predictive algorithms optimize human effort. Similarly, annotation workflows informed by RLHF techniques streamline human input, amplifying productivity while maintaining the critical oversight necessary for data quality. The feedback loop becomes a living example of how human and machine collaboration can yield superior outcomes. The philosophical implications of these advancements are profound. The concept of "self" as a static entity dissolves when viewed through the lens of machine learning and quantum theory. Gödel’s incompleteness theorem and Heisenberg’s uncertainty principle suggest limitations in understanding the self as both a sign and signifier simultaneously. Yet quantum programming languages may transcend these limitations, encoding multi-dimensional data structures that operate beyond human comprehension while retaining utility in machine learning. From this perspective, the act of self-encoding into computational frameworks allows for a reframing of identity. Through iterative refinements, such encodings reveal insights inaccessible through introspection alone. While current machine learning practices rely on abstraction and pattern recognition, quantum approaches introduce possibilities for encoding complexity at scales previously unimaginable. These advancements could redefine how we perceive identity, history, and the abstract representations of human experience. In the broader context of societal and technological evolution, such tools bear both promise and peril. Just as iterative developments in software and machine learning advance knowledge, they also risk perpetuating the dangers inherent in ideological systems when misapplied. DNA and epigenetics, for instance, iterate upon life’s design, offering templates for progress yet also bearing the specter of exploitation. The cautionary history of nationalist movements and genetic experimentation serves as a reminder of the dual-edged nature of technological growth. Ultimately, machine learning and RLHF afford humanity the tools to interrogate and evolve the constructs that define existence. However, to truly harness this potential, society must balance innovation with ethical reflection, ensuring that these systems serve as catalysts for enlightenment rather than tools of oppression. In embracing these dualities, we may yet redefine not only our technologies but also the narratives that shape the human experience.

summarize: 00:00:01 [Music] all right we are live so to speak or at least live recording yeah uh Drew you know thanks for thanks for joining me today really excited to learn about you and also of course honeyhive and then um you know discuss and in detail our our topic at hand with evaluations automated devals man um you know it's a it's a challenging problem to solve necessary to build out systems with sufficient complexity uh especially in in you know multi-turn systems like AI agents where of course those llm errors can compound 00:00:38 over time with each step so setting the stage for our audience uh the list of questions and talking points I've got are designed to provide perspective and insight for AI engineers in the way that desan Wang swix famously defined them in the the rise of the AI engineer essay so it's going to be full sack Engineers who are learning how to leverage LMS and related Technologies to build full stack gen powered applications so that's a long way of saying uh for folks out there that are watching this you know 00:01:07 there's not a strong need to have a traditional data science or ml background to get a lot of value out of today's conversation also this is going to be super casual by Design but just to add some structure uh for the folks who will be watching Drew you know before we dive into the questions let's just give some really quick background on ourselves and and our respective organizations so a little bit like the first day of class so to speak I'll go first my name is Reed Mayo I'm a founding AI engineer at 00:01:34 open pipe we are a platform that makes it super easy to fine-tune large language models so both open source and closed big and small models and we support the full inin process data collection selection and refinement and Improvement actually processing the fine tune jobs evaluating the fine tune models against each other how to surface the highest performance one all the way through um a hosted inference as well on fine tune models but that's for our users convenience so you there's no locking on that front and then going a 00:02:04 little bit deeper uh into my personal background you know I've got a lengthy background as a senior full stack engineer I've been pretty heavy into the Gen space for about a year and a half now before open pipe I co-founded and built a senior full stack engineering agency I grew that to a team of 25 a great experience uh in leadership I was lucky to work with Incredible Engineers incredible talent but it's it's lot more fun being closer to the tech these days especially working in a novel space like 00:02:30 J I and then also you know Drew before I let you kind of share some insight into your background just want to tell people um the best place to reach me for anyone out there interested is over LinkedIn so just send me a connection request I'm super friendly love to chat to anyone love to teach anyone and love to learn from folks so yeah yeah man go forward Drew can you give us some background on yourself and and honeyhive yeah um no first of all thanks for hosting this this is really cool um getting to talk about Evas is really fun 00:02:58 and really boring and really important because I love that description make it super exciting yeah yeah yeah no I think it's you can't talk about it enough as how I feel about evals it's always the veggies that no one wants to eat but everyone knows they have to that's how I feel yeah for sure yeah but uh you know quickly so personal background on myself and honeyhive first of all honeyhive is like a observability and evaluation platform for applications so idea is basically anyone who building an AI app 00:03:31 wants to figure out like um what's going wrong where um wants to run like Ci tests production observability that kind of thing we just orchestrate the development workflow and make it easy for people to build like uh complex AI apps um so just think like everything outside the deployment L actually um so just you know making sure you have every all your ducks in a row and all your evaluators are passing Etc Now personal background on me um so I started off uh in j actually at Microsoft back in 2021 00:04:03 when only Microsoft thought ji was a thing and everyone else was life time ago 2021 that was before November 2022 whenever everyone realized that you know this big thing under the surface lurking no I mean dude I had like a onour conversation about the philosophy of science with an AI in 2021 and I was like what just like where have we ended up which St tree are we on now um you know that's how felt on about it but yeah like um back at Microsoft itself I started building prototypes of like coding agents um with the open a in 00:04:41 collaboration back then and um I pretty quickly realized it's like a very new form of tech very new kinds of best practices are going to emerge new ways to evaluate new ways to monitor um just like this it's just this open-ended problem of how do you tune an intelligent system like it's like previously in software it's like you a very deterministic thing with very well documented handles on everything and you're kind of entering this domain where Anything could happen but it's roughly going to be intelligent like 00:05:10 it's just is but like is this bird Plus+ or is this AGI like you know you're always kind of in your mind balancing between the two and um yeah so I think we started honeyhive me and my co-founder who was my roommate in college uh back in 2022 and uh we got started uh you know preach GPT in the prom days and now we're in the Asian days oh yeah kind of seeing this space go through its motions that's awesome that I had no idea that you all were pre you know chat GPT launch that's that's really cool 00:05:42 absolutely agree with what you were saying I mean the big Paradigm Shift here for me coming from like a just a traditional software engineering background is you know software engineering everything is deterministic you can understand it you know what you know what it's going to do there's no surprises and if you're a junior engineer there's lots of surprises but you get enough years under your belt everything is completely understandable massive paradigm shift and and building with llms and and gen it's everything 00:06:05 somewhat non-deterministic um you know obviously you can kind of pull leverage to make it more not not deterministic and less and more deterministic but that to me is like the big paradigm shift that that um you know was was both challenging and also interesting for me to to pick up and like I've told people like you know to me llms and gen are the first novel thing in software engineering basically since I started doing this you know it's just there's there's really been nothing like it it's it's super fun uh but super challenging 00:06:35 So speaking of challenges Drew thanks thanks for that intro uh let's go ahead and like dive into it so I've I've sort of structured the list of questions here to start with the basics and then just like layer on complexity as we go yeah so yeah let's let's start with the basics cool what is the most basic question you can think of here I'll launch with it drew what are evals why are they important and you said said they're sometimes boring to talk about sometimes exciting to talk about but like Mission critical and fundamental 00:07:05 what's going on here W um you know it's the the term eval is used in many different ways in this system so it's this kind of like a landm question I feel but I I think ideally we should have had Clarity on this while back but anyway best practi is early days okay at least the way I think of about evals is making sure that your application is within certain performance expectations like I guess on a very general level that's what EVs means just kind of generally across domains that's like I have some 00:07:41 expectation that you know when the user does these kind of things um you know these kinds of uh issues won't show up and this kind of performance will be expected right you can quantify this via SAS in the case of software in the case of machine learning it's by like loss functions and F1 and so on in the case of a it's more like you know for these kinds of user inputs uh make sure that these assertions are always passing that AI is mentioning something about this the AI hasn't mentioning this in an 00:08:10 incorrect way the answer doesn't have hallucination Etc so what evals is at that point is like um your understanding of what the users needs are that being your data set that you're going to try to test against and uh your understanding of what the users's expectations are of what the responses should feel like or should you know exactly be like and encoding that bya like subjective evaluations or objective evaluations and so having that kind of a harness against your system and then continuously running that kind of 00:08:42 through the life cycle of your development process that's what I think ebals is there's a lot of open questions now like all over the place over here like I think the boring part of it is I think AI the problem with AI is it's too easy to build a good enough prototype right it's a general intelligence you so you could just throw whatever you wanted at this model and it would give you some answer right now that answer is useful meaningful who knows right but you can throw something at it this wasn't a case 00:09:13 in software right in software you really have to think about the API you need to think through what expectations are for what's going in what's coming out you can't even build something without knowing what you're building right yeah like it's impossible yeah it's not the case right it's general intelligence quote unquote right so you could just be like hey um we're going to the summarization right like some team somewhere is like we're summarization now here's what you can do right you can just be like the prompt is 00:09:40 summarize that you know that's the prompt there's massive variant here in quality of yeah which why it's so easy to you know like whip up a demo and a weekend but yeah everyone is is killing themselves trying to productionize this stuff and and you know build something you can actually trust and your users can actually trust and and the reason for that is no one's thought through the specification right no one's really thought through what kinds of inputs do I want my system to handle what kinds of 00:10:09 outputs do I realistically wanted to generate what's the domain of good and bad like no one thinks about that they're just like yo we'll throw this in frad we'll set up a feedback end point people say like dislike and we'll roughly know what the hell's happening and like if more likes than dislikes we build something useful like that's kind of the go-to mechanism here but it's lazy engineering like you know even in software you know there's everyone can be lazy engineering at any point like you just be like try try Cash pass like 00:10:35 you know we will take care of it you know at some point in the far future so like much worse cuz it won't crash the user will still get like you know somewhat bad output right you don't yeah exactly exactly is it actually useful and the thing is because people don't have clear evaluators they don't critically think about how do I measure what good looks like yeah they don't they don't realize that their application's not good until way later until like what we've seen is like you know vertical AI startups like 00:11:07 someone was spin up a GPD rapper and they're like you know for a month it's like wow crazy growth like wow everyone loves it and then you know a month later everyone realizes this thing doesn't work yeah it's a toy it's a toy yeah it's toy you know the edge cases start popping up they start exploding and in the air start compounding um you know everything you were saying was was fascinating but one something that really stood out to me was you started talking about EV vals almost in two different like um I won't say 00:11:33 philosophical camps because both of them are necessary you know I think of eval is kind of as like quality control you know total quality control you evaluate everywhere and and you make sure things don't fall run off the guard rails here y you talked about that but you also talked about thinking about a vals as modeling your system you know what type of inputs do you expect what type of outputs do you expect and then of course building it sounded like you almost build the architecture of your technology somewhat conceptually with 00:12:03 with an EV Val's harness if I'm hearing you right so my my next question here which actually kind of feeds into that pretty good is um you know when should I consider adding your vals to my gen stack like is it Mission critical is it nice to have not need to have is I'm building a POC like you talked about like whipping up poc's but then everything falls apart pretty quickly it sounds like it should like the first thing that yeah that you think about at least yeah know I mean you know this concept uh that I proposed it's called 00:12:32 eval driven development you know you know a lot of people talk about it um yeah I think the thing is here's the difference between you know and software tdd has been a thing like test driven development and like you know senior engineers get that this is important Junior Engineers learned the hard way that this is important you know it's kind of different in so uh in geni because as I said like as you caught on as well your eval is what you're building this is intelligence you're working with so if 00:13:02 you don't tell it precisely what you like and what you dislike and what kinds of things it needs to perform well at it will just do whatever you know it's like it's a creativity machine right like it'll just be like hey you know the developer who buil this prompt system agent or whatever didn't really think through whatever EDC that this user is giving me so instead of you know in a same world your prompt would include something like if the user's questions are relevant just say I I don't know or 00:13:30 this is not relevant to me you know ideally there would have been something like that but most of the time it's not or maybe the AI misjudges that oh this questions in its domain right people don't even need all that but that's a different issue but let's say something new comes from the left field to the AI it will just try to rip because it's rlf like dictates that it needs to make sure that the human enjoys the conversation but does not dictate that the app is reliable that's not what it's been our l for is RF for just riffing 00:14:02 with people so yeah then in that kind of a world it's like you know when should you consider evals I think there's like different kinds of evals like first you need to do this kind of exploratory eval where you're like okay I generally know what kind of feature I want to push for my customers you just run stuff in the playground just you know throw different inputs at it and I feel like a lot of the early part of developing an app the model is teaching me what's possible I feel like they'll just start doing stuff 00:14:29 that I didn't even think it could do and I'm like wait should we productize that or the thing that I wanted it to do it's not able to do reliably but it can do it if I constrain it really tight what I want then it's able to do so that initial eval phase is very exploratory there's nothing really in code anywhere nothing's like kind of committed but that eval is just like Vibes and getting clear on what you want the thing to do and then once you've clarified what you want the thing to do then you should 00:14:56 introduce eval at the beginning immediately Isa okay now of course I know people won't do that because it's not fun it's not sexy you know what I mean like you know what sexy like I yo I ship this demo over the weekend you know that sounds cool to people right and they'll just store in front of a user the user will use it like three four times like it kind of works and then they're like yeah yeah yeah let's take this to Broad that's really what happens good good luck um so so Drew if say that you know 00:15:26 I'm totally convinced and by the way um you didn't have to convince me because being a senior engineer that's been around the block yeah I actually have the problem of over engineering up front typically I have to like tell myself like read like chill out with the test actually start driving stuff forward but but I totally uh understand your philosophy here on modeling the system with you vals because you know LMS and gen are so loose in general purpose you've got to put some focal points on here else you're all over the place what 00:15:53 are some of the different types of like fundamental vows you know I hear likeing V pairwise like what what are your thoughts there on like the like the fundamental vow types that people are using and which ones are like useful which are less useful and you know what use cases maybe I think uh Eugene Yan has already done a very brilliant job I just want to call that out at the beginning you know his um post on this topic is just like gold standard stuff I think um my thoughts are like very mirrored with those so I think first of 00:16:30 all like you know as much as possible whatever evaluators you have I think should be deterministic so what I mean by that is if you want the model to do something let's say summarization data extraction those kind of things you should just have something like a Rex check I think Rex checks are like a really good way to get started like don't mention these kinds of things mention these kinds of things Etc like so those are the kind of like initial kinds of eal that I see which is just like sanity checks right sanity checks 00:16:58 deterministic checks just make sure we are doing something that roughly makes sense then you kind of get into um like the assertion checks or like you know criteria checks kind of thing which is here now you need to start tapping into some models intelligence to check what the hell's going on um here now now with criteria checks you're getting into assertions where you're like does the answer follow the pro right that's very simple well you start off with that so then you go from the more deterministic rejects 00:17:27 reliable ones then you go into slightly more non-deterministic evales but there you need to do your best to kind of like really really knel down your eval promt the see it's so many bad practices with eval proms I mean like all over the place like people just use them yeah what are some of the best practices for these eval prompts like is it like if you're trying to stuff too much complexity into one prompt like like decompose it rip it apart maybe have like I don't know one LM eval prompt for each specific criteria IIA that you were 00:18:00 talking about yeah yeah yeah no exactly I think you need to really really tightly scope the criteria as you said like have one LM do one criteria at a time and the criteria that you have really critically think about how much creative freedom are you giving the llm with interpreting your criteria like is your criteria something like oh it should sound good d right it's going to do whatever you know open yeah yeah like when you say to an llm tell me if this is good what you're asking is all the annotators that open Ai and thropic and 00:18:37 Google have hired to rlf my models do they find this good yeah that is the claim you're making when you say and you asking LM is this good because those these LMS have that's the um annotation data they've been aligned on so they're going to hook up to whatever Persona aligned this model the general purpose model when it was built so you what you really need to understand then is like when you ask an LM hey this is good is like need to understand who is my user what do they want what are the clear 00:19:08 requirements and then you need to be like okay and good means blah blah blah blah blah like this might is user this is kind of what they want this is how they want it and then give it positive example negative example so just standard llm best practices few sh it give criteria and very critically I think Eugene talks about this as well like um binary criteria as much as possible yeah yeah he talked about that yeah of classification metrics like he said it's just easier to measure yeah and and the thing is L is a little too 00:19:37 much like a human in my opinion right like when I ask someone like hey can you rate this thing on one to five what do people do right like I give that five you know I really like it like you know there's no calibration in that scale right it's just like the feel of it and then so you know I think there's a few studies been where you check for different models how well do they distribute across scores and how align they are ranking 1 2 3 4 5 to seven and they're just like all over the place like it's very biased toward certain 00:20:05 just like people to yeah so just like scoping down um your big criteria into a bunch of binary criterias and then what I do is um then have a hierarchy of importance on these binary criterias of like first it must pass this then it must pass this then it must pass this so on so forth and so then you kind of end up with this like eval set which is like some deterministic checks for it then some uh very scoped down basic assertion checks from an llm which do specific things and then priorities on each one 00:20:37 of them so you kind of have a well calibrated score now this though um just I'm going to now we're going now now I'm going to take one abstraction level higher here this is for a single step eval is the way I like to think of it right okay there have some step in your process in your AI application somewhere where youve embedded some intelligence some the moment you put intelligence in any part of your application you need to set up EBS this just has to happen cuz intelligence is General it'll start 00:21:08 riffing like you know like it wants to be a jazz musician you know like you're asking it to be an engineer right so then so you then you'll have it give it guardos and so that's fine but now you when you get into the multistep flow I think you hinted at this earlier in the conversation regarding the multi-turn stuff but so there's like multi-step applications which is like rag yeah hey let's get some data from somewhere let's process it once twice Thrice give it to the user and then there is agentic 00:21:36 applications which are like multi-turn instead of multi-step but it's like you do something user does something you do something user does something and then so it's like multi- turn like heing back and forth back and forth yeah I see these two architectures as like kind of canonical um like you see these repeating patterns often and so now with multi-step evals you now get into this next layer of complexity which is okay let's say each step now I've like ealed it I'm like happy with my evals i' like 00:22:08 oh yeah another quick point on the eval single set eval the inputs that you're testing against for those criteria make sure they're simple you know make sure they catch where your evaluator is bad that's the whole eval and stuff which I'm not going to get into it's too complicated for now but yeah there's just like some sanity checking you'll have to do on your llm evals so make sure that they're like saying something meaningful so yeah in multistep I think the biggest complexity jump there is just like as you said it 00:22:36 right like errors compound yeah catching compounding errors um and setting up evals for those is very very tricky but you have to do it if the application's critical yeah if it's it's a non-critical application where the user can just be like yeah I don't like this restart um then it's fine maybe you could get away with not doing that elow yeah do you curious Drew so like I'm almost thinking and this is my full stack background uh talking but um it's almost like you know you talked about the single step in evaluating that 00:23:07 single step you talked about almost the cascading layer of the vows and and actually I want to confirm that so when you were talking about like the sanity checks like RX and then layering on more complexity with maybe um Jud judge LMS you're talking about like a cascading series of vows on one potentially one single llm call okay cool yeah so you cover all your bases you start with like you know then you jump to you know assertion style almost like unit tests and then of course use a judge LM to evaluate the 00:23:36 more abstract things po like red Jax is concrete you know the judge LM might be um evaluating the more abstract features of the the output so that's like one single step but whenever we get to these multistep workflows I imagine we want and correct me if I'm wrong but I imagine we want like these that cascading series of evaluations on every single step those are almost like our unit tests but then we probably also want an overall like an integration test over the entire workflow where you know 00:24:09 for example catching hallucinations I know that's kind of challenging we'll talk about that later in in the conversation but you know if like step one and we you know maybe step one failed and we didn't catch it and we got to step two and step two was successful but then we have like the overall like integration tests like hey did this system work at least maybe then we can catch if the system system failed you know we don't might not know what step it failed in but we could throw it out maybe and then like regenerate the 00:24:35 entire process or Loop is is that what people do like I have no idea by the way I'm just kind of like spitballing here based on how I would approach it yeah yeah no I mean that is how people approach it so we so in h we do tracing right so uh tracing is kind of this process of capturing all those steps and then um being able to visualize that after you've run the application and then finding for each specific step as you set it right those unit tests that we kind of set up on each step people just go through it and then see like oh 00:25:05 okay so this pass this Etc now getting the whole check on the end to endend system right now it's if I'm being completely honest is manual okay it's not that I I just think llms aren't really good with really large context EB like if you start throwing like traces like I've done this a lot right like I'll take a trace of a multistep application in our platform and I'll throw it into an LM I'll be like what happened like you know um it gets so lost the LM gets lost so quickly it's like it starts thinking 00:25:42 at the application in the middle it'll be like so yes so the next step that I would do after these steps like no no no no dude don't like I'm like just tell me what happened in the steps but then yeah because it's so large the context it just there's so many tokens it just gets distracted Midway it's like I am the application and I'm not analyzing it and then so so there's like a lot of massaging that we are doing at Honey T to like make llms easy it it make it easier for them to understand multi-step 00:26:08 traces and groups of traces and stuff because it's just a lot of context data for LMS to process when you're taking like a full multistep Evo like in practice technically I think the simple things people do is they just rely on the EBAs of the final step and consider that an integration test they like yeah if you made it this far and you didn't pass my unit test on the final Stu you probably did a bad job but that only holds true for multi-step applications like Rag and multi- prompt systems is 00:26:40 not true at all for agents or multi-agent systems okay because in agentic systems it's like you know it's a trajectory like the LM is just doing stuff and it's going around and trying stuff like maybe it accomplished the user's intent like five messages ago right but it just didn't realize that it did you know and then you end up at the last step and the yeah like all confused like oh maybe I should ask user for more clarity or something and if you just run the Eva on that step you'd think oh my 00:27:11 agent failed but actually the Midway trajectory there is some points there that it actually accomplish the user's objective so when you get into agents and multi-agents you get into trajectories and then when you get into trajectories there's a whole different class of B vales that show up which no one in the systems talked about we've done a bunch of work at honey I've related to it but we've like it seems like the ecosystem is not like ready for it cuz they're still doing eval on steps but trajectory eval is really where like 00:27:40 I think once the rag becomes reliable which will take about I think last famous last words two years I think like I think two years maybe it'll become like reliable enough Contex get big enough Precision will be good enough like people will be able to just throw tons of data at llms and get something useful out of it I think we'll get there very soon then I think the buck of Evas will move to trajectories and you know understanding what paths is the AI taking to solve problems and evaluating those parts and that's a way more 00:28:10 complex problem we can talk about that if you want um but yeah I'm actually pretty interested in diving into that because I've never even heard of the concept of trajectory evaluations but it makes complete sense to me I mean like you said no one's talking about this how I know it's a really hard problem maybe it's not super solvable with today's technology or or techniques like how might you optimize as best possible like you talked about this AI agent that's doing its own thing maybe accomplish the 00:28:34 user's objective but then like kept going because it never it's like the agent ultimately at some point needs human feedback to know whether uh you know whether it's on track or or not is that is that like the path it's just like regular when you're productionizing this stuff it's just like regularly checking in with the end user or like what's yeah what's the what's the best practice I know that's all this like experimental also yeah yeah no we've thought a lot about this in our team we just so okay productionizing trajectory 00:29:06 based systems is a really hard problem I don't think people have realized how hard it is they've just like free wied it into Broad and you know that's why most agent applications are a meme right now but you know when we're talking about you know AI creating like trillions of dollars of GDP when I hear that I'm like I see like giant multi-agent systems doing extreme complex and sensitive stuff that's what that number means in your mind right right but I'm like dude Eva is so far behind we in no way getting there like 00:29:38 soon like I don't know see that path right now for people because we're just like I think eval has become the second class Citizen and like people kind of hate it but I'm like no this is what you need to get there that's what I going to say I mean it's a fundamental building block because if you can't quality if you can't measure measure success yeah you can't build anything can I mean like lit there's no you can't direct anything it's to me it's like the fundamental building block for almost all complexity yes exactly I agree and 00:30:10 especially with intelligent systems right if you're embedding you know breathing life into the machine right you're like giving it wings like just like you know give it like God reals too like hey you know we can fly around in this area like but don't you know fly too far but if you don't have any evil just keep flying just yeah yeah they'll go anywhere at once so I think going back to the trajectory thing I think so we're kind of in this in between early days phase of AI is how I think mentally 00:30:37 where we're still trying to make single step work we're still trying to make multi-step work it's kind of becoming reliable in some domains it's like not reliable in most domains but again I think this is just like you know people building PCS in 70s like I see the path like get eventually we're going to get to internet scale infra and the shit's going to be running everywhere but so okay so let's Leap Frog in our imagination now multi-step is Sol um now when you are running into these systems which are doing these complex 00:31:03 multi-turn stuff you need in my opinion simulations okay so simulations is a concept that we've developed we've used simulations at honeyhive to test trajectory based systems which are multi-t because so there's two ways to solve the trajectory issue right one is the way you pointed out which is as the AI is doing the thing the user is watching it and then the the user will intervene or the AI will ask the human like hey dude I'm stuck here do you have any feedback can I do something better uh can you give me the API Keys whatever 00:31:37 like the yeah might have to just Loop in with the human to get some context but this is a this not this is not true AGI like you know true AGI I tell it go do the thing just G does the thing it's like yo it's done you know if it needs feedback it's a whatever but the thing is if you don't do a lot of simulations early on what you'll end up having is a system which has to keep intervening with a human very regularly and the you human will have to give it a lot of oversight because it's just not been 00:32:06 tuned very perfectly to solve those kinds of paths like so the idea behind simulation the way we think of it is how do you gain certainty and confidence in your trajectories you run a lot of trajectories that's it like you know just run the system through like many different versions of what it's supposed to see in production you create different different environments and you let it rip in those environments and then you analyze the trajectories that it took in that environment and the guard rails on your trajectories see how 00:32:34 often it hits the guard rails see how often um the performance is satisfactory and then based on that you will also know oh in these domains my agent can do a really good job in these domains are car and so when when you go to production you will know you're like it's like you know the best analogy to think of this is self-driving cars right like self-driving cars they have simulated environments that they train in and they're tested in and so so you know if I simulate SF roads I'm not going to go drop my wayo in like you 00:33:03 know Indian Streets like dude not going to have a good time like I'm telling you not it's just the the diff in the environment that you have seen it working and where you're going to put it later on is too big so that's like the rough dldr of like multi-turn trajectory based systems like you basically need to create different different uh mock environments and just let them these agent systems multi-turn systems rip and you also need to create simulations of your users um the user simulations can 00:33:35 be very simple like just a deterministic like Playbook of like first the user will ask them to do this then three messages later the user will ask the agent hey can you do this differently so then your evals will start looking like that where you're trying to simulate the way it interacts with the environment and the user and then testing those so I think I get the um the concept towards basically just um run run a t different simulations see what's ultimately successful see what's ultimately failing 00:34:03 yeah the stuff that's failing you go in there and you patch it up still BS the question of again why vals are the fundamental building block I mean the problem here is like how do we automate the feedback in the sense that you know you run a million simulations if the AI is not confident of what's ultimately successful which trajectories are successful which aren't um you know we need humans to go in there and basically manually score this stuff hey this was successful this wasn't and so forth but 00:34:28 of course that doesn't scale if you've got humans involved so are we back to the same problem here yeah yeah no brilliant brilliant question I think um the scalable oversight problem has like two pieces to it I think in the trajectory case I I think it's going to be easier hear me out like oh it's going to be easier because see right now ai is in this weird in between phase where we're not giving it too much control over actual systems right you're creating these mock situations and letting it do its thing and maybe the 00:34:57 user gets you but when let's say you know pick an actual agent like let's say I have it go do my software engineering for me I go have it do my taxes for me there is a very clear definite outcome that can be measured there which is not subjective like if I have an AI agent doing stock trading for me do the trajectory eval is how much money is in the bank account you know like there is a very clear end outcome which is very measurable like I feel like a lot of this eval problem happens because there 00:35:27 is an actual metric which is like whatever user engagement user satisfaction whatever people have metric and then you create a proxy metric and then you're always trying to align the proxy metric with that one now we have this additional problem where the human eval is there and so now we need to align the human eval with the automated eval and then align the automated eval with the end user metric right I'm just saying what will happen in the multi-age and agent trajectory world in the far future when a is super reliable we'll 00:35:54 just go for that end metric directly we'll just like skip all the steps in the middle all this alignment stuff we just like dude I know what I want just go I'll give you know my AI access to my post I'm like you see that down ratio like send it to the moon like you know like I I it's so much easier because then the AI can apply its intelligence it's NOS is exact eval is the end metric that the business cares about it can go optimize that right now eii doesn't have that capability so then I have to create 00:36:21 this proxy metric on a proxy metric and then I have to like scope it down make sure the proxies are light so that's my intuition for I think the Eva problem will get drastically easier the more AI becomes humanlike and intelligent in our in the way that we are but we're in this weird in between phase it's like you know magical pony sometimes works sometimes doesn't um so then we need like yeah I was just going to say I think ultimately it's about Focus yeah if if it's uh true agentic outcomes of 00:36:50 you know I either got my taxes done or I didn't I mean that's it's almost back to what uh you know Eugene Yan was saying about binary classification did it succeed that it fail whereas the in between is all the fuzziness so to speak yeah that's that's occurring and so yeah getting getting a little bit more down to earth here in concrete and and kind of reducing some of that fuzziness today y you you talked about uh the the unit test type of assertions um I think those are pretty IM immediately clear to folks 00:37:17 talk to me about L you know using using an llm to judge the output of other LMS what's the state of that today like how do you optimize that um you know I've heard people talk about like you know obviously there's prompt engineering of course with with prompting the judge prompt but then people also talk about like having a specialized fine-tuned yeah judge LM or for example doing like a jury pool of LMS or like a bunch of different LMS judge the uh you know run the ultimate evaluation and then like they just Vote or something 00:37:48 like that talk to me about some of the best practices there what you seen works and and maybe what's a bridge too far based on what honeyhive is is seen with your customers or just even your Network yeah yeah no I think so we've had the great privilege of working with like uh extremely sophisticated AG agent teams and let me tell you their evaluators aren't just a single llm call okay it is an extremely complex multi-step pipeline that's how they done their evils right they don't just like I don't think jury I think the fact is you 00:38:22 can't rely on a single LM call so doing a jwy on it still doesn't make sense to me like it's just either way it's like both are unreliable now you're just like maybe less certain more certain about the unreliability I guess but like you're just trying to get one llm step to do too much thinking that's the fundamental problem there so what these agent startups have discovered is let's take our logs and then we will break down the eval that we wanted to do into many steps into multiple pipelines and 00:38:51 then they run those pipelines side by side and then they judge all of the outputs of all their prompts in the end and then give a final score so that's realistically production grade evals that's what I've seen it's and so you have actually a separate team working on the eval application that's what really how real evals are done I think the like folks who actually care about performance end up dedicating two three people just to work on that final eval pipeline application so that it actually starts giving you numbers which mean 00:39:22 something yeah I was going to say they're actually useful and reliable what that pipeline typically look like like and maybe we have to rewind a little bit here um so you've talked about you know evaluations end the day prompt input LM generation response evaluation is you know was that good or not um but was that good or not of course is a really loose way of saying it um realistically you started talking about criteria which to me I think it's like different dimensions of quality or consistency in this output or this 00:39:51 generated response and then you also talked about read Shard that rip it you know to Pieces don't have like one big massive um you know prompt that says like is it good was it you know was it factual did it cover all the um pertinent information so you're talking about a pipeline here for the evals almost like a um like a multip workflow on the eval side I guess first I should ask are they typically independent so it's like one check runs you know was it factual one check runs you know was the sentiment what we desired talk to me 00:40:23 about what those pipelines look like in the real world you know with these customers of yours that are building really complex agents yeah so I think the kind of simple checks as you put it I think those kind of run all the time they're just constantly running when they're running their test so on so forth the eval agent like that's what we like to call it within our team like every agent team will build an eval agent which will look at the other agent trajectory and then it's an agent which goes through it and then evaluates 00:40:50 whether it did a good job or not so that's how these teams approach it is they basically run the whole agent with all the checks on each step and then they wait for the agent to get done and then the eval agent comes in and then it looks over the logs the traces that have been generated and then it goes through it step by steps reasons over the thing and then sanity checks like oh okay like basically each team has like you know like based on their experience playing with the agent like five kinds of 00:41:21 failure modes that they've seen like oh my agent gets distracted by this or my agent doesn't uh for feedback um around these issues or so what they'll do is they'll tell the eval agent hey you know from playing around with the agent we've diagnosed you know these six eight kinds of issues that come up often here's a trajectory of from production that my agent has done go over this trajectory and reason about these criteria and come out at the other end after reading the whole thing and reasoning about it and 00:41:49 tell me roughly speaking was that issue detected here or not and give him give a justification for why and so this way so this is getting into the trajectory eval thing that we were talking about earlier I'm fascinated yeah so they basically do this kind of trajectory eval and initially so this is a really cool thing initially the teams that we were working with were doing this post produ okay they waited for production agents to finish running and then they ran these agents over their logs in Honey head 00:42:20 yeah but after they tuned that eval agent and got it good where they started to trust what the eval agent was saying about the original agent yeah they started running it in production so what started to happen so this is mul's Agent Cube paper if you actually look into it is they started to run the agent in production and then the eal agent would be the test time search verifier and so what they would do in production is the agent would come up with 10 actions at each step or 100 actions at each step and then the El 00:42:53 agent would determine let's go with that action the best action that is fascinating so that's how then mulon started leveraging test time search which 01 is also based on and so after they tuned their eval agent and they had a a verifier that's reliable they then got it into production and then started leveraging test time search compute and then the reliability of the agent just shoot through the roof cuz instead of you know like in practice what was happening before was without the eval agent at each step the agent is just 00:43:20 guessing you know it's like probably that's the right one like like maybe we should try that like but then once you know they adding up Trac is in production and they had run like the agent like a million times and they had user feedback data and everything they're like dude we canot tune an eval agent off of this data and then they tun that and then they throw it into production with the agent and now it's like it's helping it make the best decision so the the production agent is like generating a bunch of different 00:43:45 possible paths and then the eal agent is saying maybe is scoring or waiting those and saying like pick this one it almost conceptually to me sounds like training a reward model yeah yeah but it's not a model like see I you earlier you asked earlier about fine tuning specific models right and maybe that's a way to tune reward models or you know prom pack or you know use few short like positive negative examples system yeah just like I'm going to say just no okay none of those approaches are going to work right 00:44:15 people are going to try it they're going to you know sell it like I just don't care I don't think it's actually going to work like when an llm evaluates another llm what you care about is its explanation the number is whatever dude like just see like the number is like okay came up with some number but it always comes up with some number you know what I mean like it doesn't actually have a tuned calibrated mechanism somewhere in its net which is telling it only pick this number right it's just like that seem like a good 00:44:41 token to me like you know it's what you really tuning is the explanation the criteria that your evaluator judge is using to inspect other llms and to make that criteria and explanation very detailed and very thought through you will end up creating a multi step pipeline how would you not right like why are you trying to overload so much like cognitive work is how I like to think into one Al call just don't do that like split into multiple calls and then you'll go through the human evaluator tuning process and you know 00:45:13 the human and the eval system will Rift with each other and once it's all aligned you can then constantly keep aligning that thing and that thing will align your end uh agent so that's kind of how I roughly feel about it like I find like explanations that the LM are the more useful thing of LM bels objective score or whatever classification yeah so what you're trying to I mean ultimately what we're trying to align here is the LM generates something that the human agrees as the right thing so we want you know it's 00:45:45 like aligning the LM output a result with human desired output a result alignment so what I'm hearing from you is uh when you're building your um judge LM or realistically your judge AI agent you want to have the judge LM steps explain their thinking explain their rationale for why they feel that this output either meets or fails the criteria and then align that rationale with the same humans rationale yes exactly exactly that is insightful yeah like the just because that's the problem right like you don't know how it's coming up 00:46:21 with the score so you're like tell me how you're coming up with the score as like this how I'm coming up with the score then you're like no just no that's not the right way to think about and then the human goes like think about this failure mode think about that failure mode think about this and then once it's rational and you're rational when you're reading a traces line boom now you have an align system now that is now you're like that is insightful that is amazing yeah yeah that's why I'm like 00:46:44 I'm really really bearish on like people tuning judge models like I think it it's kind of useful for like you know getting away from calibrating the scores and making them seem more realistic but at the end of the day like are you being real like a judge Alam will work across Finance healthcare insurance like I don't know like you know Structural Engineering civil like is that reallying the model to give you the output that you want uh so to speak yeah like fool the human long enough kind of yeah so 00:47:16 how do you generate that that uh those reason traces is it just like you know literally prompting before with like chain of Chain of Thought and then yeah Chain of Thought you like give me the output give me the score but then like how did you how did you come up with that score llm is that how you do it and then just align those thought traces with the humans Jud yeah yeah like you start with simple Chain of Thought few short like I think um yeah if you do like three shot Chain of Thought that's 00:47:42 like a good starting llm evaluator and then you can read its explanations and be like most of the time actually the llm teaches you new ways of thinking about your system that's is what I love about doing AI engineering the AI is teaching you how to engineer it half the time like and so then you read the explanations you like okay like but then sometimes it starts like considering things in its judgment where you're like dude this like totally irrelevant right and then then you create like okay then 00:48:08 you get this sense of like okay maybe I need to run two eval prompts right one which is looking at this dimension of the performance one which is looking at this Dimension and I need these two to kind of think independently so that they don't either of them don't get distracted yeah and then have a final step which takes the two then final step you can do like a you know average or whatever maybe add another LM step to put all the facts together this all comes down to the complexity of your original application and how 00:48:37 multifaceted is the eal so yeah in practice I think a really good F short Chain of Thought you look at the explanations tun those then add more and more steps when it's clear that when you understand yourself actually what you're eving cuz half the time that's what I'm telling you problem with Eva is people just ship stuff they don't know what they're they just like yeah yeah forces you to think about the problem yeah and solve it up front Drew I know we're about out of time here um I did want to get one last question over to 00:49:05 you really quickly what are some of the best resources to learn more about how to you know build these high performance robust evaluation systems do you have like communities or or online resources that you recommend I know that you noted Eugene Yan earlier like he's he's my go-to yeah yeah any other thoughts there yeah I think um shers eval were that's obviously like top of mind I think second is Eugene Yan's work around evaluator tuning I think there's a big gap in the literature around how to do 00:49:36 good synthetic data generation for evals that's like no one's attacked that problem I'm really surprised why in terms of like how we kind of think about it I'm actually working on a blog post now for building robust evaluation systems so hopefully when that's out I can refer to that based on all these learnings that I've kind of talked about today but um yeah we have like some uh we have the honeyhive blog where we have some basic stuff written out about this but yeah that was written like a year 00:50:01 ago so I think a lot Lear learning since then yeah yeah that's I'm kind of doing the same where you know I'm learning from you I'm having other conversations with um you know experts in the eal space and I'm going to be building kind of a write up as as well nice I I would be really interested in reading that absolutely shareed out to you um but speaking of uh you know our audience you know Finding good ways to connect and learn with you what's what's the best way to to reach you um for casual conversation serious convers how do how 00:50:29 do people find you online and actually connect with you yeah yeah yeah no I'm always down to talk to people just reach out on LinkedIn for sure that's the easiest way that's uh the most reliable and then yeah email is also works like I'll turn a cal link immediately if you want to R about Evas cuz oh my god this is my pet problem like I think this is a long-term thing it's going to be a 10-year journey I think it's not going be a short-term thing I think I didn't get to call out some of the work uh 00:50:55 coming from uh Uther AI yes the best resource actually for learning about evals is Uther ai's eval paper I I'll send it to you after the call they synthesized all their learnings across dude like they are the go-to eval harness for judging llms and they've been running a ton of harnesses their research and learning it's like a 100 page dog it's just the best to learn about Evas I don't know what talks about it and I'm like these guys have done more evil than anyone else Drew this has been incredibly wonderful incredibly 00:51:27 insightful especially at the end there with talking about the parallel you know your production AI agent then your your evaluation um AI agent as well that was like incredibly insightful to me want to thank you so much for for you know carving out some time uh to chat with me and and teach me and ultimately our in in the audience here um this been great man app no I mean thanks a lot yeah yeah no thanks a lot for the really good questions and um for posting this cuz um yeah I mean the more people talk about 00:51:56 EVS the better cuz again like I feel there's so many gaping holes like and honey have all we've been doing is building the you know the basic Plumbing for running Evas reliably but I feel like there's still so much work to be done around how to design good test cases for evaluations like the different kinds of eval agents you can work for different kinds of domains like there's just so much Green Field space but you know it's just so easy to ship something to pra with no Evas like that's why the 00:52:23 eval research always falls behind this by how feel thanks for this I going to say I think it's starting to catch up though um you know it's like last year everyone was excited about all the demos this year reality starting to hit people in the face and I think it's it's made emails on incredibly hot topic um you know whenever I was at gen uh or engineering World's Fair earlier in the year like everyone was talking evals because it's like how how do you build actual production system without evals 00:52:49 and the question is you don't build good ones it's been an absolute pleasure no I'm let you get back to it man thanks man y thank you see you

### **Summary** This recorded discussion dives deep into the challenges, methodologies, and future of evaluations (evals) in AI systems, particularly focusing on applications like generative AI agents and multi-turn AI workflows. The conversation highlights the importance of evals in ensuring AI system reliability and addresses the nuanced complexities of evaluating both single-step and multi-step processes. #### **Key Points Discussed:** 1. **Evals as a Fundamental Building Block:** - Evaluations are essential for ensuring AI applications meet performance expectations and avoid compounding errors, especially in multi-turn systems. - They act as a framework to define and measure "good" outcomes in generative AI workflows, which often lack determinism found in traditional software systems. 2. **Paradigm Shift in AI Development:** - Unlike traditional software engineering, generative AI development requires a new way of thinking, as systems are inherently non-deterministic and capable of producing outputs across a wide spectrum of quality. - Developers must model systems with evals integrated from the outset, termed as "eval-driven development," ensuring systems are purpose-built to meet specific user needs. 3. **Types and Layers of Evals:** - **Sanity Checks:** Initial deterministic checks, such as regex or rule-based evaluations. - **Assertion Checks:** Slightly more complex checks using models to evaluate specific criteria. - **Trajectory Evals:** A higher-level evaluation for multi-turn or agent-based systems, focusing on the path taken by the AI to reach an outcome. 4. **Challenges with Multi-Turn and Agentic Systems:** - Errors compound in multi-turn systems, making end-to-end evaluations critical. - Evaluating agent-based systems requires a nuanced approach, involving trajectory analysis to capture intermediate successes or failures. - Simulations are necessary to test how agents handle various scenarios, building confidence in their ability to navigate real-world complexities. 5. **Advanced Eval Practices:** - Creating detailed multi-step pipelines for evals, where individual checks are scoped narrowly and layered hierarchically. - Using "eval agents" as parallel systems to evaluate logs and traces from production agents, identifying common failure modes and refining agent behavior in real time. 6. **Automation and the Role of Human Oversight:** - While automated evals are critical for scaling, human alignment with the AI’s reasoning remains essential. This involves training AI evaluators to provide detailed explanations and align their judgments with human rationale. 7. **Resources for Learning Evals:** - **Eugene Yan's Blog:** A valuable resource for learning about evaluator design and tuning. - **Uther AI's Eval Paper:** A comprehensive synthesis of eval best practices and methodologies. - HoneyHive’s upcoming blog posts and work on foundational tools for running reliable evals. 8. **Future Directions:** - The eval field is evolving rapidly, with significant gaps in synthetic data generation and trajectory evaluations. - As AI systems gain more autonomy, evaluation methodologies will shift towards optimizing for clear end metrics (e.g., measurable business outcomes) rather than intermediate proxies. --- ### **Takeaways:** This conversation underscores the critical role of evaluations in building trustworthy AI systems. By integrating evaluations at every stage, from single-step tasks to multi-turn agentic workflows, developers can mitigate risks, ensure reliability, and scale AI applications effectively. The discussion also highlights the importance of community collaboration, sharing resources, and advancing research to address gaps in current eval methodologies.